For a function of one variable , you look for
local maxima and minima at critical points ---
points where the derivative
is zero. You do
something similar to find maxima and minima for functions of two
variables.
A point is a local max if
for all points
"near"
. If you want to rule out "ties", then you
have to require instead that
for all
points
"near"
. In this case,
is a strict local maximum.
Similarly, a point is a local
min if
for all points
"near"
. Again, to rule out "ties", you'd require
instead that
for all points
"near"
. In this case,
is a strict local minimum.
Proposition. Suppose f is defined on an open
set containing and is differentiable at
. If
is a local max or a local
min, then
Proof. If is a local max, then it is a local max of
, a function of one variable x. Then
by the result on local maxima from
single variable calculus. A similar argument shows that
.
A critical point which is neither a local max nor a local min is a saddle.
Thus, to find local maxima or minima, locate the critical points by solving these equations simultaneously:
Example. Use graphs to describe the critical
points of ,
,
, and
.
has a local minimum at
.
has a local maximum at
.
has a saddle point at
.
has a line of critical points along
. They are local minima, but not strict local minima.
Once you've located the critical points, you must determine whether they are maxes, mins, or saddles. We have an analog of the Second Derivative Test for functions of one variable.
Theorem. Suppose f is differentiable on an
open set containing a critical point . Suppose the second partial derivatives are
continuous.
Define
Then:
(a) If , then
is a saddle (neither a max nor a min).
(b) If and
, then
is a min.
(c) If and
, then
is a max.
(d) If , the test fails. (This doesn't
mean the point is neither a max nor a min --- it means there is no
conclusion.)
Remarks. In (b) and (c), you can use
" " instead of "
", because if
the two must have the same sign.
To see that gives no conclusion, consider
. You can check that
is a critical point and that
at
. However,
is a minimum. For
, but if
, then
. Hence, every point
gives a larger value for f than
.
Proof. (Sketch) I'll sketch the argument for
this test in the case where .
There is a Taylor expansion for functions of two variables:
Here is a point on the segment from
to
.
Since is a critical point,
, and the series becomes
To save writing, let
The series can then be written
As long as h and k are small, I can assume that the second
derivatives (and hence ) have the same
signs at
and
. I'll consider the case where
. Note that A and C must be both
positive or both negative. For if one is positive and one is
negative, then
is negative, and
, contrary to our assumption. So suppose
that A and C are both positive. Then I can write
Since and
, the last
term is positive. I have
That is, for small h and k.
This says that points close to
give bigger f's, so
must be a min.
Note that if , then
is negative (because the stuff in the brackets is positive). Then
This says that points close to give smaller f's,
so
must be a max.
The argument that yields a saddle is more
involved, so I'll omit it.
Example. Locate and classify the critical points of
First, compute the partials:
Find the critical points:
The critical point is .
Here's a picture of the surface. You can see the min pretty clearly.
Example. Locate and classify the critical points of
First, compute the partials:
Find the critical points:
Multiply by 4, multiply
by 3, then add the equations:
Then
The critical point is .
Example. Locate and classify the critical points of
First, compute the partials:
Set the first partials equal to zero and do a little simplification:
It's okay to divide out common factors which are numbers, but you should avoid dividing by something with a variable in it --- unless you're certain the expression cannot be zero.
I'll solve the equations simultaneously using a solution tree. Start with one of the equations --- in this case, it doesn't matter which one.
Every time I have an equation , I get two cases ---
and
--- and
the tree splits into two branches. (In this case, I had a three-way
split, but one branch closed up because there were no solutions.)
I continue working down a branch until I've solved for x and y. Notice that you bring in each of the original equations just once. This will help you avoid going in circles.
Finally, you should not combine values from different
branches. For example, I can't put together with
to get
.
Now test the critical points:
Using the table, it's easy to remember --- it's the second column times the third, minus the
square of the fourth. Notice that
means the point is either a max or
a min. To decide which it is, look at
. (This may seem asymmetric, but in fact,
if
then
and
have the same
sign.)
Here's a picture of the surface. I've deformed it a bit to exaggerate the critical points.
Example. Locate and classify the critical points of
First, compute the partials:
Set the first partials equal to zero:
Solve simultaneously:
Test the critical points:
Here's a picture of the surface, deformed to exaggerate the critical points:
Example. Locate and classify the critical points of
First, compute the partials:
Set the first partials equal to zero:
Solve simultaneously:
Test the critical points:
Here's a picture of the surface, again deformed to exaggerate the critical points:
Example. Locate and classify the critical points of
Compute the partial derivatives:
Set the first partials equal to zero:
Solve simultaneously:
Test the critical points:
Here's a picture of the surface, again deformed to exaggerate the critical points:
Copyright 2018 by Bruce Ikenaga